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OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
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Datum
2022
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Gesellschaft für Informatik e.V.
Zusammenfassung
Morphing attacks are one of the many threats that are constantly affecting deep face recognition
systems. It consists of selecting two faces from different individuals and fusing them into a final
image that contains the identity information of both. In this work, we propose a novel regularisation
term that takes into account the existent identity information in both and promotes the creation of
two orthogonal latent vectors.We evaluate our proposed method (OrthoMAD) in five different types
of morphing in the FRLL dataset and evaluate the performance of our model when trained on five
distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the
majority of the experiments, and competitive results in the others.